Project Summary Patients and their clinicians often face a decision about which of several possible treatments would work best. Traditionally, patients defer to clinicians who prescribe using their best clinical judgment. The patient-centered care and ?quantified self? movements have evolved from patients' desires to have more control of their own healthcare and more information upon which to base decisions. The N-of-1 clinical trial uses a design in which patients test two or more treatments on themselves in a scientifically valid experiment and compare results to see which works best. This design enables estimation of the effect of treatments on individual patients and may promote more patient involved decision making about their own care. Combining N-of-1 trials also allows estimating treatment effects in groups of patients. The design has not been widely employed, however, because many non-research healthcare environments do not have sufficient research infrastructure to effectively carry out the experiments. The overall project objective is to accelerate implementation of patient-centered outcome research by developing methods and software for analyzing N-of-1 trial data and providing output oriented toward helping people make correct treatment decisions. Specifically, the project aims to: (1) develop methods for combining N-of-1 trials using meta-analytical models; (2) use simulation to assess the behavior and performance of different models in order to be able to make recommendations about which to implement in clinical environments; (3) apply the methods to data from N-of-1 trials. The proposed work is significant because it will help to address the needs for real-time analytic methods that patients and clinicians can apply directly without expert guidance. Our meta-analytical models will allow for borrowing strength from information of other patients thus will lead to more accurate treatment effect assessment and improve the precision of treatment decision- making for an individual. We anticipate the results of this work will encourage and enable people to do N-of-1 trials that focus on personalized treatment comparisons.